Information captured by images is becoming of an increasing importance in numerous application fields.  Hence, comes the role of image retrieval systems in order to make use of image collections to serve applications needs.
This thesis addresses the problem of providing a scalable and an efficient method for image retrieval. The system described in this thesis focuses on content based image retrieval. To ensure robustness and
high quality of results, localized features and interest regions are extracted from images as a basis for the matching process. To address scalability and search speed challenges, hierarchical clustering and spectral hashing are used as two alternatives
for data space partitioning. In addition to using inverted file indexing and tailoring a text-based search engine (Lucene) to operate on images. Experiments are done to evaluate
both the quality of the retrieval results and the ability of the system to operate on large scale databases of images.